Update functions.py

functions.py

@@ -1,6 +1,6 @@
 from langchain_community.document_loaders import PyMuPDFLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI
+from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI, OpenAIEmbeddings, ChatOpenAI
 from operator import itemgetter
 from langchain_core.runnables import RunnablePassthrough
 from langchain_qdrant import QdrantVectorStore
@@ -43,7 +43,8 @@ def setup_vector_db():
 
     nist_chunks = text_splitter.split_documents(nist_doc)
 
-    embeddings_small = AzureOpenAIEmbeddings(azure_deployment="text-embedding-3-small")
+    # embeddings_small = AzureOpenAIEmbeddings(azure_deployment="text-embedding-3-small")
+    embeddings_small = OpenAIEmbeddings(model="text-embedding-3-small")
 
     qdrant_client = QdrantClient(":memory:") # set Qdrant DB and its location (in-memory)
 
@@ -68,7 +69,8 @@ def setup_vector_db():
 
 # define a global variable to store the retriever object
 retriever = setup_vector_db()
-qa_gpt4_llm = AzureChatOpenAI(azure_deployment="gpt-4o-mini", temperature=0) # GPT-4o-mini model
+# qa_gpt4_llm = AzureChatOpenAI(azure_deployment="gpt-4o-mini", temperature=0) # GPT-4o-mini model
+qa_gpt4_llm = ChatOpenAI(model="gpt-4o-mini", temperature=0) # GPT-4o-mini model
 
 # define a template for the RAG model
 rag_template = """